This presentation is used in a refresher course at Nuzvid. This is one day session of the course. It introduces research avenues in Image Processing and allied areas to faculty participants..

1. Let me say
Hearty Welcome
and
Happy New Year 2006
to you all
Prof NB Venkateswarlu
Head, IT, GVPCOE
Visakhapatnam
venkat_ritch@yahoo.com
www.gvpcoeedu.org

2. Objective of This course
•Overview of D.I.P.
•Overview of current
Research in D.I.P
•Guidelines for Ph.d

3. What is research?
3 depressions and
6 papers!!!!
I don’t believe in “Practice Makes
Man Perfect”. Rather I believe in
“Frustration Makes Man Perfect”.

4. What is research -Cont
“First, they ignore you.
Then they laugh at you.
Then they fight you.
Then you win."
--- Mahatma Gandhi

5. What is research - Cont
Don’t think the work should be conclusive.
Tomorrow if you prove that Newton is
wrong and apple can go up also. You will
be awarded Ph.d

6. What is research -Cont
What is the research and what is
Development?
Development means we are sure of the
outcome.
Research: We are not sure of the final
outcome. Also when do you get also we
do not know.

7. What is research -Cont
If you dream an Idea today
night and you will see the
same published in a Journal
next day. Don’t feel sorry. It
indicates that your thoughts
are correct. Your progress is
correct. Not only seven people
like you exists in world else
where. But also they may think
similar to you.

8. What is research - Cont
Do proper Literature survey.
Prepare a state of art report
and give a seminar on it and
invite people (experts) in
that area and have their
feed back.

9. What is research - Cont
Make sure that the
examiners will not
point out that the
work is already done
else where.

10. What is research - Cont
Think. Think. Think.
You will get the answer.
--- Prof J. Venkata Ramana

11. What is research - Cont
Scribble your ideas on a
piece of paper and file.
A British Example of
weather forecasting. Just
because of data storage.

12. What is research - Cont
• Current Contents
• Abstracts
• Journals
• Magazines
• Survey Articles
• State of art Proceedings
• Technical reports
• White Papers

13. What is research - Cont
• Wikipedia
• Bogs
• Patent reports
• Make sure you don’t forget acknowledging
the material you have used
• See the copyrights also

14. What is research - Cont
Learn a Foreign Language –
Especially, we computer science
people should learn probably
Chinese, Japanese, etc,.
Use the help of JNU, Delhi and
CEFL, Hyderabad for
Translation works.

15. What is research - Cont
What way you can present your work/ideas?
1. Letters
2. Articles
3. Communications
4. Papers
5. Bogs
6. White papers

16. What is research - Cont
Make sure that you will do the following.
1. Proper English while writing article
2. Proper title
3. Proper abstract
4. Follow Electronics submissions
5. Maintain your website
6. Make sure that your article is available in your
web site after acceptance.
7. Make sure that you will apply for patent

17. What is research - Cont
Attend the Conferences/workshops/courses
in your subject area. You will get into
contact with experts in your area. You can
give feed of this to your Supervisor. They
may become your examiners. Preferably
invite them to your college for a talk and
that time have your supervisor and
discuss about your work. This will help you
reduce time to get reports after
submission.

18. What is research in I.P?
I compare it as research in Chemistry where
we will have some set of Unit Operations
(reactions such as titrations) which when
we apply on some chemical compounds
we will get some final product (s).
Similarly, I.P research can be also said as
having finding set of Algorithms and
sequence of algorithm’s which gives
expected results in an application.

19. Research in I.P
“Sarvendriyanam
Nayanam
Pradhanam”
The visual information is the
prime reason for our’s
intelligent actions.

20. Probable research areas in I.P
• Optical Character Recognition
• Alternative technologies
such a Lip movement recog, Brialle readers
• Security
• Compression
• Image Data bases and retrieval, mining
• Real time monitoring
• Forensic applications

21. Probable research area in I.P
• Medical Imaging
• Astronomy
• Manufacturing
• Robotics
• Monitoring and suvelliance
• Remote Sensing
• Calibrating lenses

22. Is there any boarder
between Computer
Graphics and Image
Processing?

23. What is digital Image?
• Converting visual information into digital data
such that there is one to one correspondence.
Involves sampling and quantization like any
other sensory applications.
Barber in Chennai used
I.P to decide which hair
styles suits to his
customer.

24. A Typical Image Processing
System contains
Image
Acquisition
Image Pre-
Processing
Image Acquisition
Image En-
hancement
Image Seg-
mentation
Image Featu-
re Extraction
Image Class-
fication
Image Unde-
rstanding

25. Types of Images
• 2D Images
• 3D Images (CAT images)
• 2 1/2D Images (Range
(LIDAR) Images)
• Tactile Images

26. D.I.P vs Electromagnetic Spectrum
• Gamma Ray Imaging –PET
• X Ray Imaging – angiograms, board test
• UV - Flourcense microscopy, lithography
• Visible and infrared- Remote Sensing
• Microwave band – SAR
• Radio band – MRI
• Ultrasound - Medicine

27. Thermal imaging – testing a battery

28. Image Acquisition
What is meant by resolution?
Pixel
Gray level
Band

36. RGB, R ,G and B bands

37. False Colour Images

38. Mixing Low and high resolution
images. See sharpness increased

39. Pre-Processing
• Noise elimination
• Geometric corrections
• Cloud corrections in the remote sensing

40. Top:- Orinal, Mean Filetered
Bottom:- Gaussian and Median Filtered

41. Mean Filtering
• Window size can vary
and also weights can
change
• Gaussian and Mean
filtering retains low
frequencies
components while
blurring edges

42. Median Filtering
• Computationally intensive
• Eliminates blurs and retains edges
• Introduces speckle noise
• After median filtering histogram peaks
becomes narrower such that thresholding
becomes easy

43. Histogram before and after median
filtering

44. of the features removed. Example
in the following figure the arrows
points to the objects which are
removed if the window size is 5

45. Conditional Median – A threshold is
used – Example: original, window
of 5 and conditional median with
threshold of 50

46. Median Filtering of Color Images –
median of average of R+G+B

47. Hit or Miss Median Filter is used to
remove speckle noise and scan line
noise in survelence cameras

48. Eliminating Background Noise from
improper illumination conditions

49. Selecting background areas and
fitting background surface and then
adjusting the image

50. Eliminating noise in Video Seq.
Image Averaging

51. Enhancement Techniques
Histogram Stretching
a[m,n] = orginal brighness
b[m,n]= output brightness
B= number of bits for each pixel

52. Modified Contrast Stretching

53. Histogram Stretching

55. Local contrast Equalization

57. P-histogram Equalization

58. Local Variance Equalization

61. Joining multiple images. In the
following example first 3 principal
components are added to generate
the following image

62. Edge Detection – Horizontal
Derivivative

63. Embossing Filter or Directional
Derivative

65. Original, directional, Laplacian,
Sharpening

66. Sobel and Prewitt Operators

67. means first isolating the edges in
an image, amplifying them, and
then adding them back into the
image. Examination of Figure 33
shows that the Laplacian is a
mechanism for isolating the gray
level edges.
Original Laplacian-enhanced

68. The term k is the amplifying term
and k > 0. The effect of this
technique is shown in Figure 48.
The Laplacian used to produce

69. Image
Segmentation
&
Binarization
Main objective is deviding image into
meaningful groups. Edge detection can be
also called as one type of segmentation.
Binarization is the process of deviding
image into background and foreground.
Widely used in OCR.

70. segmenting the foreground from
background. In this section we will
two of the most common
techniques--thresholding and edge
finding-- and we will present
techniques for improving the quality
of the segmentation result. It is
important to understand that:
* there is no universally applicable
segmentation technique that will
work for all images, and,
* no segmentation technique is

71. Simple Two level Thresholding
• This technique is based upon a simple
concept. A parameter called the
brightness threshold is chosen and
applied to the image a[m,n] as follows:

73. Local Methods Are very much
needed
• Kittlers Method
• Method proposed by Me.

74. Clustering Algorithms - Kmeans

75. Region based
Region agglomeration
Region devision

76. Use of Simulated Images
• Testing purpose we need simulated
images.
• Especially for testing transform based
methods such as filters etc.,.
• Also for calibrating lenses etc., simulated
images are used

78. Feature Extraction
• Shape based
• Contour based
• Area based
• Transform based
• Projections
• Signature
• Problem specific

79. Perimeter, length etc. First
Convex hull is extracted

80. Skeletons

82. Averaged Radial density

83. Radial Basis functions

84. Rose Plots

85. Chain Codes

86. Crack code - 32330300

87. Signature

88. Bending Energu

89. Chord Distribution

90. Fourier Discriptors

91. Structure

92. Grammar Based Recognition
• Language rules are used

93. Splines

94. Horizontal and vertical projections

95. Elongatedness

96. Convex hull

97. Compactness

98. Hidden Markov Models

99. Morphology

100. Classification/Pattern Recognition
• Statistical
• Syntactical Linguistic
• Discriminant function
• Fuzzy
• Neural
• Hybrid

101. Patterns
Features (measurements)
Classes
Classification
Training
Training set
Testing set
%correct classfication
%mis classification

102. • Boot strap testing
• Distance based
• Decision surface based
• statistical

104. Neural Methods
• Non deterministic methods
• Supervised and unsupervised applicaions
can be used
• Major draw back is their huge simulation
time requirements
• Main advantage is that they can be
realized in embedded system or as a
ASIC

105. • For recognition problems – Back
propagation network is used.
• For clustering purpose – Kohonen is used,
ART1, ART2.
• For pattern recollection etc – Hopfield
networks are used. Example criminal
identification
• Assosciative memories can be also used.

106. • Neurons
• Firing characteristics
• Neural connections
• Learning equation
• No of hidden layers
• No of neurons
• Training by epoch or sample

107. An example – Digit recognition

108. Vector coding

109. Fuzzy Methods
• Possibility and probability

110. Image Understanding
• Recognition vs understanding

112. Characteristics of Knowledge-Based
Image Understanding
• Image understanding is a knowledge-based
process. For the several processing steps we
need different kinds of knowledge. Knowledge
can be coded implicitly (procedural knowledge)
or explicitly (declarative knowledge).
• Because of the ill-posed problem of interpreting
images without prior knowledge, image
understanding must consist of data-driven
(bottom-up) methods and model-driven (top-
down) processes.
• Image understanding is a goal-directed activity.
The control of a single operation - like focusing,
coarse and fine analysis - must depend on the
image´s current context.

113. Expert Systems for Image Processing and
Analysis
• There are two kinds of knowledge-based
systems for image understanding:
• Expert Systems for Image Processing and
Analysis (ESIPA) which deal with low-level
processing up to segmentation.
• Image Understanding Systems (IUS) which have
the symbolic description of an image as their
goal.
• The criteria to distinguish between those two are
their tasks (use given methods/find new ones),
their knowledge sources (not domain
specific/domain specific) and their objectives
(eg. "find all squares"/"find the tumor"). In this
chapter I'm concentrating on ESIPAs and in the
next on IUSs.

114. Image Document Understanding
• Vast archives of information, handwritten and machine printed on
paper, have accumulated over centuries. Advances in computer and
communication technologies now offer drastically improved ways to
store, retrieve, and distribute their contents. Billions of paper
documents wait to be made accessible via electronic media.
• Document image understanding and retrieval research seeks to
discover methods for automatically extracting and organizing
information from handwritten and machine printed paper documents
containing text, line drawings, maps, music scores, etc. Its
characteristic problems include some of the earliest attacked by
computer-vision pioneers. The field has long been distinguished by
close and productive ties between the academic and commercial
communities. Today, document analysis research supports a viable
industry which, stimulated by the growing demand for digital
archives, the proliferation of inexpensive personal document
scanners, and the ubiquity of FAXes, is poised for rapid growth. But
the performance of these technologies still lags far behind human
abilities. Many technical problems, critically important on both
theoretical and practical grounds, remain open.
• We are pleased to offer a collection of state-of-the-art papers

115. Computer Vision Application
• Robotics

116. Motion Image Applications
• Compression
• Understading
• Robotics
• Bandwidth reduction

119. Correspondence problem
Practical examples
cloud tracking from a sequence of satellite meteorological data
cloud character and motion prediction,
motion analysis for autonomous road vehicles,
automatic satellite location by detecting specific points of interest on the
Earth's surface
city traffic analysis
many military applications.
Most complex methods work even if both camera and objects are moving.

120. Image Mosaicing
• Mosaicing Involves
1. Acquiring images
correcting applying croping, resizing,
flipping
2. Identifying the tie points
3. Identifying the match
4. Joining the images
5. Final adjustments such as tonal
adjustments among the images

121. Image Mosaicking

122. Ready to Join Images

123. Marking a Tie Point

124. Joined Image

126. Image Registration – Need in
Image Fusion

127. Image Compression
For less storage space
For conserving bandwidth
For ease of retrieval

129. What is the basis for
compression?
Redundancy!!
Autocorrelation at
various levels – intra
pixel, intra band, intra
frame etc.

130. • Scalar vs block
• Lossy vs lossless
• Uniform and non-uniform
• Predictive encoding
• Spatial domain vs frequency domain
• Vector Quantization
• JPEG
• Wavelets
• Fractals

131. Scalar – DPCM,ADPCM, Huffman

132. 2D Prediction

134. Principles of Transform coding

136. JPEG Coding

137. JPEG Decoding

140. Original Image, Difference with
JPEG image, Hue channel before
and after JPEG compression

141. Progressive Compression

142. Compression of Video Frames

143. Principal Component Analysis

144. Parallel Computers for D.I.P
• Why do need?
• Fastness or for real time performance
• Especially for real time monitoring with
images we need the parallel computers.
• Coarse/fine graininess

145. • CLIP4
• DAP
• ILLIAC4
• MPP
• Connection Machine
• Systolic Arrays

146. • How much benefit we get very much
depends on the algorithm and parallel HW
architecture.
• There is no gurantee that an efficient
serial algorithm is going to be efficient
parallel algorithm. And also, vice versa
may not be true always.

147. • Most important thing is mapping on the
parallel HW.
• Complexity very much requires
communication cost to be included.

148. Medical Imaging
• CT
• X-RAY
• PET
• MRI
• SPECT

149. Reconstruction Tomography

153. Non destructive Testing
• Space bourne materials

154. SPECT
• Digital medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), and
single-photon emission computed tomography (SPECT) are being used in large numbers in hospitals. Further,
direct high-resolution digital imaging systems are becoming available for application to chest and breast imaging.
Increasing use of these digital medical imaging modalities, digitization of x-ray film images for computer
processing or archival, and high demands on speed and lossless performance of image archival and
communication systems in the medical environment have placed increasing emphasis on the development of
lossless image coding and compression techniques with improved performance. High-resolution medical images
possess characteristics different from those of other commonly-encountered images; in particular, they tend to
possess high correlation in regions corresponding to anatomical or radiographic features, which could be
exploited for data compression. We have developed a number of reversible or lossless image data compression
schemes using combinations of many techniques such as two-dimensional linear predictive coding, transform
coding, interpolative coding, Huffman coding, arithmetic coding, Lempel-Ziv coding, and Peano-scanning
techniques. We have also developed new methods of computing and encoding two-dimensional linear prediction
and transform coefficients.
• In order to exploit the presence of high correlation within radiographic structures, we have recently developed a
segmentation-based coding scheme which decomposes a given image into a number of adaptive regions; the
image is then broken down into a discontinuity index part, an error image part, and a seed data part, which are
then encoded using the JBIG (Joint Bi-level Image experts Group) standard. An innovative combination of JBIG
procedures with the F-transform have also been developed to achieve improved lossless compression.
• We have evaluated the performance of the techniques with a large set of test images and high-resolution digitized
medical images of up to 4096x4096 10-bit pixels. Data reduction achieved has been very good, from 10 bits to 2.9
bits for medical images, and from 8 bits to has been very good, from 10 bits to 2.9 bits for medical has been very
good, from 10 bits to 2.9 bits for medical has been very good, from 10 bits to 2.9 bits for medical images, and
from 8 bits ts for non-medical images. The techniques should find applications in medical image archival and
communication systems.

158. Astronomy

159. Remote Sensing and D.I.P
• Sarswati Rever
• Rama’s setu
• Water areas identification
• Identifying forest fires
• Identifying urban growth
• Coast line changes
• An ant movement can be identified

160. • Satellites such as Landsat, SPOT, IRS
• NIMBUS
• SAR, SLAR
• 242 channels HIRIS, IVIRIS satellites
• Air bourn plane images

161. • Here also training and classifiction
• Survey of India maps made in 1937 we
are using. Very difficult to prepare survey,
geological survey maps. Satellite dats is
very useful.

162. Security Application
• System Security – Finger Prints – BC
Hostels
• Eye pupils for Ration Cards
• System security using face recognition

163. Alibaba 40 Thieves
Example

164. Multimedia Security
• Who has issued multimedia content
• Who has sent
• When it is sent
• Is it modified
• Where it is got modified
• What was it before modifications

165. Multimedia Hiding
• No extra headers are needed
• Synchronizations problems are less
• Self authenticating and correcting
Examples
• Bin Laden example
• Stegonagraphy – Pure, Private, Public

169. Optical Character Recognition
• Printed
• Hand Printed
• Hand written
• Printed digits
• Hand printed digits
• Signatures

170. • Main applications are
• Postal sorting
• Office Automation
• Reading machine development
• Banking
• Helping disabled
• Form processing
• Examination evaluations

171. Main difficulties
• Paper quality
• Paper orientation
• Paper background
• Variety of fonts
• Different style of writings
• Scanner errors (discontinuties)
• Breaking the documents into lines, words
and characters

172. Main difficulties
• Different inks
• Ink spreadings
• Age of the paper
• Real time requirement
• Size variations
• Poor writing (kids writing which does not
have strcture)

173. Online recognition
Offline recognition

174. Approach
Sample data collection
Pre-processing
Training set
Classification set
System development
Realizing in HW

175. Alternative Technologies

176. • Reading machines
• Chairs with Ultra sound sensors

177. Scalable Vector Graphic Format
SVGF is non-visual extension of a digital
image format. The aim of this study is to
allow blind computer users to have access
to the semantic and spatial content of
pictures available on the Internet.
Brialle, verbal descriptions etc., are
automatically displayed

178. Free D.I.P SW’s
• VIPS
• Khoros
• FIPS
• CIPS
• Image magic
• GIMP

179. Commercial Image Processing SW
• Accuvision
• MIDAS
• ERDAS
• SACIMAGE

180. Popular Image Data bases
• SUNY database for OCR
• LILO
• Leeds database for faces

181. • More accessible version
• ICS > CVRL > Links > Image databases>
•
•
•
• Image Data Bases
• Pointers to Public Image Databases
• DARPA Image Understanding datasets (motion,
stereo, medical, aerial, range, ...)
• VASC Image Database at CMU (motion, stereo,
texture, outdoor)
• Imagery Archive at MIT Media Lab (mostly textures)
• Vision Image Archive at UMass (sequences, stereo,
medical, indoor, outdoor, road, underwater, aerial,
satelite, space, and more)
• Imagery Archive at Teleos
• Medical Image Databases from Penn State

182. Journals related to D.I.P
• Image Processing and Computer Vision
Journals:
• Computer Vision and Image Understanding
• Digital Signal Processing: A Review Journal
• Electronic Letters on Computer Vision and Image A
• Foundations and Trends in Computer Graphics and
• IEE Proceedings - Vision, Image and Signal Process
• IEEE Journals Overview (IEEE Stylefiles)
• IEEE Manuscript Central
• IEEE Transactions on Image Processing

183. • Click a book link to view all the available details:
· Scalable Parallel Computing: Technology, Architecture, Programming
· Parallel and Distributed Computing Handbook
· Message Passing Server Internals
· Parallel Processing (State of the Art Report, Series 15, No 4, Infotech)
· Scientific Computing : An Introduction with Parallel Computing
· The Helios Parallel Operating System
· Massively Parallel Models of Computation: Distributed Parallel Processing in Artificial Intelligence and Optimization (Ellis
Horwood Series in Artif)
· Fundamentals of Parallel Computing
· Limits to Parallel Computation: P-Completeness Theory
· Parallel Optimization: Theory, Algorithms, and Applications (Numerical Mathematics and Scientific Computation)
· Concurrent Systems: An Integrated Approach to Operating Systems, Database, and Distributed Systems (2nd Edition)
· Software Design Methods for Concurrent and Real-Time Systems
· An Introduction to Parallel Computing: Design and Analysis of Algorithms, Second Edition
· Natural and Artificial Parallel Computation
· Execution Models of Prolog for Parallel Computers
· Parallel Computational Fluid Dynamics: Implementations and Results (Scientific and Engineering Computation)
· Massively Parallel Artificial Intelligence
· Advances in Distributed and Parallel Knowledge Discovery
· Perceptrons - Expanded Edition: An Introduction to Computational Geometry
· Explorations in Parallel Distributed Processing - Macintosh version: A Handbook of Models, Programs, and Exercises
· Introduction to Parallel and Vector Solution of Linear Systems (Frontiers in Computer Science)
· Introduction to Parallel Processing: Algorithms and Architectures (Plenum Series in Computer Science)
· Parallel-Vector Equation Solvers for Finite Element Engineering
· Hierarchical Scheduling in Parallel and Cluster Systems (Series in Computer Science (Kluwer
Academic/Plenum Publishers).)
· Logics and Models of Concurrent Systems (NATO Asi
Series, Advanced Science Institutes Series, Series F, Computer and Systems Sciences, Vol 13)
· Seminar on Concurrency (Lecture Note in Computer Science, Vol. 197)
· The Analysis of Concurrent Systems (Lecture Notes in Computer Science, Vol 207)
· Conpar 86 (Lecture Notes in Computer Science, Vol 237)
· Wopplot 86: Parallel Processing: Logic Organization, and Technology (Lecture Notes in Computer Science, Vol 253)
·
Algebra of Communicating Processes: Proceedings of Acp94, the First Workshop on the Algebra of Communicating Processes, Utr
·
Bibliography of Parallel
Architectures and Image
Processing